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利用一个基础模型动物园在瘤显微镜中跨设备进行细胞相似性搜索

Gabriel Kalweit^1,2, Anusha Klett¹, Paula Silvestrini^1,3

¹Collaborative Research Institute Intelligent Oncology (CRIION), Freiburg, Germany.

Frontiers in oncology

|July 3, 2025

概括

本研究介绍了以透指导的加权组合FAISS (EWC-FAISS),这是一种无监督的AI方法,用于在各种成像设备上进行细胞识别. 无需染色或广泛的手动标签,EWC-FAISS准确地区分细胞类型和状态,在分布之外的数据集上表现优于其他方法.

科学领域:

计算生物学计算生物学
人工智能的人工智能
显微镜的使用方法

背景情况:

传统的细胞成像分析是耗时的,需要大量的手动标签.
现有的人工智能解决方案通常依赖于使用大型,特定的标记数据集的监督学习.
为了实时洞察,需要一种不需要染色或手动标签的跨设备细胞识别方法.

研究的目的:

通过使用通用视觉基础模型,在不同的成像设备中开发一种无监督的细胞分析方法.
为了使细胞类型和状态的自动,跨设备识别,使用仅推断模型.
通过高效的细胞群动态分析,为人工智能辅助的癌症治疗提供基础.

主要方法:

从不同的显微镜获取的公共 (WBC,LISC) 和内部细胞系数据集.
采用了基础模型 (SAM,DINO,ConvNeXT,SWIN,CLIP,ViTMAE) 的连接组合来进行特征提取.
在为自动化细胞识别而生成的嵌入器上实施了自适应式k-最近邻居搜索 (EWC-FAISS).

主要成果:

关于原始数据集,EWC-FAISS表现出了竞争力的表现,并且在未经分发的数据集上始终超越了基线.
在不同成像平台上实现了细胞类型和状态分类的高宏精度.
使用EWC-FAISS嵌入生成和索引构建比训练深度学习模型要快得多.

结论:

一种新的无监督方法 (EWC-FAISS) 在各种成像平台上有效地识别细胞系和原始细胞.
该方法可以在没有染色或手动注释的情况下区分细胞类型和状态 (活细胞,细胞亡细胞,细胞亡细胞).
欧洲工会FAISS在分析患者样本和监测治疗反应方面提供了重要的应用.

关键词:

人工智能的人工智能是人工智能.细胞成像细胞成像深度学习是一种深度学习.基础模型基础模型最接近邻居的搜索搜索搜索

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